A Study on the Difference of LULC Classification Results Based on Landsat 8 and Landsat 9 Data
A Study on the Difference of LULC Classification Results Based on Landsat 8 and Landsat 9 Data
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基于Landsat 8和Landsat 9数据的LULC分类结果差异研究
DOI：
10.3390/su142113730
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发表时间：
2022-10
期刊：
影响因子：
3.9
通讯作者：
Jianjun Chen
中科院分区：
文献类型：
--
作者：
Haotian You;Xu Tang;Weixi Deng;Haoxin Song;Yu Wang;Jianjun Chen
Landsat 9 将运行陆地成像仪的辐射分辨率从 Landsat 8 的 12 位提高到 14 位。更高的辐射分辨率提高了传感器的灵敏度，以检测许多细微的差异，特别是在茂密的森林或水域的情况下。然而，目前尚不清楚Landsat 8和Landsat 9之间辐射分辨率的差异是否实际上影响了水和树种的分类结果。因此，本研究根据 Landsat 8 和 Landsat 9 图像提取了光谱反射率和植被指数。然后，利用梯度树提升算法开发了土地利用和土地覆盖（LULC）和树种的分类模型。随后对结果进行分析，进一步研究辐射分辨率的差异如何影响LULC和树种的分类结果。结果表明，Landsat 8和Landsat 9的LULC分类结果在大多数情况下都相对有利。但在水体分类精度较低的试验区，LULC分类效果较差。进一步分析，在分类结果较差的测试区域的情况下，两个数据集的水体分类结果存在显着差异。换句话说，在大多数测试区域，Landsat 9 比 Landsat 8 产生更好的水分类结果。然而，接近零的温度可能会导致逆水分类结果。此外，这表明两个数据集的森林分类结果差异较小，但基于Landsat 9的森林树种分类结果优于基于Landsat 8的森林树种分类结果，总体精度提高了6.01%。结果表明，大多数情况下，Landsat 8和Landsat 9辐射分辨率的差异对LULC分类结果影响不大。尽管如此，就某些测试区域而言，Landsat 9更适合提高水体和树种的分类精度。
Landsat 9 enhances the radiation resolution of the operational land imager from the 12 bits of Landsat 8 to 14 bits. The higher radiation resolution improves the sensitivity of the sensor to detect many subtler differences, especially in the case of dense forests or water. However, it remains unclear whether the difference in radiation resolution between Landsat 8 and Landsat 9 actually affects the classification results of water and tree species. Accordingly, the spectral reflectance and vegetation indices were extracted in this study, based on Landsat 8 and Landsat 9 images. Then, the classification models of land use and land cover (LULC) and tree species were developed by using a gradient tree boosting algorithm. Subsequently, the results were analyzed to further investigate how the differences in radiation resolution affect the classification results of LULC and tree species. It is shown that the LULC classification results of Landsat 8 and Landsat 9 are relatively favorable in most cases. However, the LULC classification results are relatively poor in test areas with a lower classification accuracy of water. Further analysis, in the case of test areas with poor classification results, indicates that there are significant differences in the water classification results between the two datasets. In other words, Landsat 9 produces better water classification results than Landsat 8 in most test areas. However, a temperature close to zero may lead to inverse water classification results. In addition, it indicates that the difference in forest classification results between the two datasets is small, but the results of forest tree species classification based on Landsat 9 are superior to those based on Landsat 8, with an improvement in overall accuracy of 6.01%. The results demonstrate that the difference in radiation resolution between Landsat 8 and Landsat 9 has little impact on the results of LULC classification in most cases. Nevertheless, in the case of some test areas, Landsat 9 is better suited for enhancing the classification accuracy of water and tree species.
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